Search

Background: Accurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value. Methods: Concentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators. Through the integration of big data analysis with function experiments, we have redefined the biological roles of m6A regulators in HCC. Based on the key regulators, we constructed m6A risk models and explored their clinical value in estimating prognosis and guiding personalized therapy for HCC. Results: Most m6A regulators exhibit abnormal expression in HCC, and their expression is influenced by copy number variations (CNV) and DNA methylation. Large-scale data analysis has revealed the biological roles of many key m6A regulators, and these findings are well consistent with experimental results. The m6A risk models offer significant prognostic value. Moreover, they assist in reassessing the therapeutic potential of drugs such as sorafenib, gemcitabine, CTLA4 and PD1 blockers in HCC. Conclusions: Our findings suggest that the mutual validation of big data analysis and functional experiments may facilitate the precise identification and definition of biomarkers, and our m6A risk models may have the potential to guide personalized chemotherapy, targeted treatment, and immunotherapy decisions in HCC. [ABSTRACT FROM AUTHOR]

Extensive research supports an evidence-base for cancer treatment-related risk factors, including extent of lymph node dissection and use of radiotherapy, as contributing to secondary lymphedema. Additionally, comorbidities, such as higher body mass index, and vascular-related conditions are identified to further augment risk. While social determinants of health (SDOH) and socioeconomic factors are widely regarded as influencing an individual's healthcare outcomes, including cancer risk and survival, these factors have not been explored as risk factors for developing secondary lymphedema. A rapid literature review explored the current evidence for SDOH as risk factors for lymphedema. Studies that were published over the last 10 years and that specifically analyzed social factors as variables associated with lymphedema were included. Studies that only characterized the social determinants of the study population were not included. Forty-nine studies were identified through a rapid literature review, and 13 studies that expressly analyzed social determinants as risk factors for secondary lymphedema were reviewed and extracted. All studies were conducted in patients with breast cancer-related lymphedema. Social risk factors included race, educational level, insurance type, and income level. These are consistent with the socioeconomic inequalities related to cancer survival. SDOH may influence the risk of developing cancer treatment-related health conditions like secondary lymphedema. Research trials studying cancer treatment-related conditions should collect consistent and robust data across social, behavioral, environmental, and economic domains and should analyze these variables to understand their contribution to study endpoints. Risk prediction modeling could be a future pathway to better incorporate social determinants, along with medical and co-morbidity data, to holistically understand lymphedema risk. [ABSTRACT FROM AUTHOR]

Abstract Background Accurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value. Methods Concentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators. Through the integration of big data analysis with function experiments, we have redefined the biological roles of m6A regulators in HCC. Based on the key regulators, we constructed m6A risk models and explored their clinical value in estimating prognosis and guiding personalized therapy for HCC. Results Most m6A regulators exhibit abnormal expression in HCC, and their expression is influenced by copy number variations (CNV) and DNA methylation. Large-scale data analysis has revealed the biological roles of many key m6A regulators, and these findings are well consistent with experimental results. The m6A risk models offer significant prognostic value. Moreover, they assist in reassessing the therapeutic potential of drugs such as sorafenib, gemcitabine, CTLA4 and PD1 blockers in HCC. Conclusions Our findings suggest that the mutual validation of big data analysis and functional experiments may facilitate the precise identification and definition of biomarkers, and our m6A risk models may have the potential to guide personalized chemotherapy, targeted treatment, and immunotherapy decisions in HCC.

BACKGROUND: Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. OBJECTIVE: To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. DESIGN: Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY RESULTS: A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. CONCLUSIONS: Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Background and Aim: While several predictive models for the development of hepatocellular carcinoma (HCC) have been proposed, including those for patients with chronic hepatitis C virus (HCV) infection who have achieved sustained virologic response (SVR), the best model may differ between regions. We compared the ability of six reported models to stratify the risk of post‐SVR HCC in Japan, where rigorous surveillance and early detection of HCC is common. Methods: A total of 6048 patients with no history of HCC who achieved SVR by oral direct‐acting antiviral drugs were enrolled in this nationwide study. Patients continued HCC surveillance every 6 months after SVR. The incidence of post‐SVR HCC was compared between risk groups using the aMAP score, FIB‐4 index, Tahata model, GAF4 criteria, GES score, and ADRES score. Results: During the observation period with a median duration of 4.0 years after SVR, post‐SVR HCC developed in 332 patients (5.5%). All six models performed significantly at stratifying the incidence of HCC. However, Harrell's C‐index was below 0.8 for all models (range, 0.660–0.748), indicating insufficient stratification ability. Conclusion: Although all six proposed models demonstrated a good ability to predict the development of post‐SVR HCC, their ability to stratify the risk of post‐SVRHCC was unsatisfactory. Further studies are necessary to identify the best model for assessing the risk of post‐SVR HCC in regions where early detection of HCC is common. [ABSTRACT FROM AUTHOR]

OBJECTIVES The multifactorial dynamic perfusion index was recently introduced as a predictor of cardiac surgery-associated acute kidney injury. The multifactorial dynamic perfusion index was developed based on retrospective data retrieved from the patient files. The present study aims to prospectively validate this index in an external series of patients, through an on-line measure of its various components. METHODS Inclusion criteria were adult patients undergoing cardiac surgery with cardiopulmonary bypass. Data collection included preoperative factors and cardiopulmonary bypass-related factors. These were collected on-line using a dedicated monitor. Factors composing the multifactorial dynamic perfusion index are the nadir haematocrit, the nadir oxygen delivery, the time of exposure to a low oxygen delivery, the nadir mean arterial pressure, cardiopulmonary bypass duration, the use of red blood cell transfusions and the peak arterial lactates. RESULTS Two hundred adult patients were investigated. The multifactorial dynamic perfusion index had a good (c-statistics 0.81) discrimination for cardiac surgery-associated acute kidney injury (any stage) and an excellent (c-statistics 0.93) discrimination for severe patterns (stage 2–3). Calibration was modest for cardiac surgery-associated acute kidney injury (any stage) and good for stage 2–3. The use of vasoconstrictors was an additional factor associated with cardiac surgery-associated acute kidney injury. CONCLUSIONS The multifactorial dynamic perfusion index is validated for discrimination of cardiac surgery-associated acute kidney injury risk. It incorporates modifiable risk factors, and may help in reducing the occurrence of cardiac surgery-associated acute kidney injury. [ABSTRACT FROM AUTHOR]

Purpose of Review: Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI. Recent Findings: We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85–0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. Summary: There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care. [ABSTRACT FROM AUTHOR]

The incidence of venous thromboembolism (VTE) in patients with lung cancer is relatively high, and risk stratification models are vital for the targeted application of thromboprophylaxis. We aimed to review VTE risk prediction models that have been developed in patients with lung cancer and evaluated their performance. Twenty-four eligible studies involving 123,493 patients were included. The pooled incidence of VTE within 12 months was 11 % (95 % CI 8 %–14 %). With the identified four VTE risk assessment tools, meta-analyses did not show a significant discriminatory capability of stratifying VTE risk for Khorana, PROTECHT and CONKO scores. The pooled sensitivity and specificity of the Khorana score were 24 % (95 % CI 11 %–44 %) and 84 % (95 % CI 73 %–91 %) at the 3-point cut-off, and 43 % (95 % CI 35 %–52 %) and 61 % (95 % CI 52 %–69 %) at the 2-point cut-off. However, a COMPASS-CAT score of ≥ 7 points indicated a significantly high VTE risk, with a RR of 4.68 (95 % CI 1.05–20.80). The Khorana score lacked discriminatory capability in identifying patients with lung cancer at high VTE risk, regardless of the cut-off value. The COMPASS-CAT score had better performance, but further validation is needed. The results indicate the need for robust VTE risk assessment tools specifically designed and validated for lung cancer patients. Future research should include relevant biomarkers as important predictors and consider the combined use of risk tools. PROSPERO registration number: CRD42021245907. • Khorana score cannot stratify VTE risk regardless of 2- or 3-point cut-off value. • The COMPASS-CAT score has discriminatory capability in VTE risk stratification. • Time-varying factors should be included in developing new VTE risk models. [ABSTRACT FROM AUTHOR]

Simple Summary: Oral cancer is among the twenty most common cancers worldwide. Finding and treating this cancer early improves survival rates. Screening the whole population to check for oral cancer is unlikely to be an efficient use of resources; however, screening only individuals at higher risk has been shown to reduce oral cancer deaths and be cost-effective for healthcare services. Mathematical models have previously been developed to identify these high-risk groups; however, it is not known whether any of these would be suitable for use in clinical practice. In this study, we identified and compared previously published models. We found several that had potential, but only two had been tested outside the original study population. We suggest that future research should focus on (a) testing how well the models identify those at high risk within potential screening populations and (b) assessing how the models might be included within the healthcare systems. In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer. [ABSTRACT FROM AUTHOR]

Objective: To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning (ML) algorithms. Methods: In this prospective study, 690 patients with SA combined with CHD attending the Department of Integrative Cardiology, China-Japan Friendship Hospital, from October 2020 to October 2021 were included. The data set was randomly divided into a training group and a testing group in a 7:3 ratio in the per-protocol set (PPS). Model variables were screened using the least absolute shrinkage selection operator (LASSO) regression, univariate analysis, and multifactor logistic regression. Then, nine ML algorithms are integrated to build the model and compare the model effects. Individualized risk assessment was performed using the SHapley Additive exPlanation (SHAP) and nomograms, respectively. The model discrimination was evaluated by receiver operating characteristic curve (ROC), the calibration ability of the model was evaluated by calibration plot, and the clinical applicability of the model was evaluated by decision curve analysis (DCA). This study was approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (2020-114-K73). Results: 690 patients were eligible to finish the complete follow-up in the PPS. After LASSO screening and multifactorial logistic regression analysis, physical activity level, taking antiplatelets, Traditional Chinese medicine treatment, Gensini score, Seattle Angina Questionnaire (SAQ)-exercise capacity score, and SAQ-anginal stability score were found to be predictors of the occurrence of CVE. The above predictors are modeled, and a comprehensive comparison of the modeling effectiveness of multiple ML algorithms is performed. The results show that the Light Gradient Boosting Machine (LightGBM) model is the best model, with an area under the curve (AUC) of 0.95 (95% CI = 0.91–1.00) for the test set, Accuracy: 0.90, Sensitivity: 0.87, and Specificity: 0.96. Interpretation of the model using SHAP highlighted the Gensini score as the most important predictor. Based on the multifactorial logistic regression modeling, a nomogram, and online calculators have been developed for clinical applications. Conclusion: We developed the LightGBM optimization model and the multifactor logistic regression model, respectively. The model is interpreted using SHAP and nomogram. This provides an option for early prediction of CVE in patients with SA combined with CHD. [ABSTRACT FROM AUTHOR]

Introduction: cardiac surgery associated acute kidney injury (CSA-AKI) has a number of preoperative and intraoperative risk factors. Cardiopulmonary bypass (CPB) factors have not yet been elucidated in a single multivariate model. The aim of this study is to develop a dynamic predictive model for CSA-AKI. Methods: retrospective study on 910 consecutive adult cardiac surgery patients. Baseline data were used to settle a preoperative CSA-AKI risk model (static risk model, SRM); CPB related data were assessed for association with CSA-AKI. CPB duration, nadir oxygen delivery, time of exposure to a low oxygen delivery, nadir mean arterial pressure, peak lactates and red blood cell transfusion were included in a multivariate dynamic perfusion risk (DPR). SRM and DPR were merged into a final logistic regression model (multifactorial dynamic perfusion index, MDPI). The three risk models were assessed for discrimination and calibration. Results: the SRM model had an AUC of 0.696 (95% CI 0.663–0.727), the DPR model of 0.723 (95% CI 0.691–0.753), and the MDPI model an AUC of 0.769 (95% CI 0.739–0.798). The difference in AUC between SRM and DPR was not significant (p = 0.495) whereas the AUC of MDPI was significantly larger than that of SRM (p = 0.004) and DPR (p = 0.015). Conclusions: inclusion of dynamic indices of the quality of CPB improves the discrimination and calibration of the preoperative risk scores. The MDPI has better predictive ability than the existing static risk models and is a promising tool to integrate different factors into an advanced concept of goal-directed perfusion. [ABSTRACT FROM AUTHOR]

Purpose: mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy. Methods: Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33–88 (range) (years)), PSA (7.2; 0.3–439 (ng/ml)), prostate volume (45; 10–300 (ml)), PSA density (0.15; 0.01–8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2–22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information. Results: Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa. Conclusion: mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach. [ABSTRACT FROM AUTHOR]

Abstract Aims The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web‐based risk scores in this selected population. This study aimed to assess mortality prediction performance of the Meta‐Analysis Global Group in Chronic HF (MAGGIC‐HF) risk score and the model of the Barcelona Bio‐HF Risk Calculator (BCN‐Bio‐HF) containing N terminal pro brain natriuretic peptide in HF patients receiving intermittent inotropic support with levosimendan as destination therapy. Methods and results Four hundred and three advanced HF patients from 23 tertiary hospitals in Spain receiving intermittent inotropic support with levosimendan as destination therapy were included. Discrimination for all‐cause mortality was compared by area under the curve (AUC) and Harrell's C‐statistic at 1 year. Calibration was assessed by calibration plots comparing observed versus expected events based on estimated risk by each calculator. The included patients were predominantly men, aged 71.5 [interquartile range 64–78] years, with reduced left ventricular ejection fraction (27.5 ± 9.4%); ischaemic heart disease was the most prevalent aetiology (52.5%). Death rate at 1 year was 26.8%, while the predicted 1‐year mortality by BCN‐Bio‐HF and MAGGIC‐HF was 17.0% and 22.1%, respectively. BCN‐Bio‐HF AUC was 0.66 (Harrell's C‐statistic 0.64), and MAGGIC‐HF AUC was 0.62 (Harrell's C‐statistic 0.61). Conclusions The two evaluated risk scores showed suboptimal discrimination and calibration with an underestimation of risk in advanced HF patients receiving levosimendan as destination therapy. There is a need for specific scores for advanced HF.

Aims: The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web‐based risk scores in this selected population. This study aimed to assess mortality prediction performance of the Meta‐Analysis Global Group in Chronic HF (MAGGIC‐HF) risk score and the model of the Barcelona Bio‐HF Risk Calculator (BCN‐Bio‐HF) containing N terminal pro brain natriuretic peptide in HF patients receiving intermittent inotropic support with levosimendan as destination therapy. Methods and results: Four hundred and three advanced HF patients from 23 tertiary hospitals in Spain receiving intermittent inotropic support with levosimendan as destination therapy were included. Discrimination for all‐cause mortality was compared by area under the curve (AUC) and Harrell's C‐statistic at 1 year. Calibration was assessed by calibration plots comparing observed versus expected events based on estimated risk by each calculator. The included patients were predominantly men, aged 71.5 [interquartile range 64–78] years, with reduced left ventricular ejection fraction (27.5 ± 9.4%); ischaemic heart disease was the most prevalent aetiology (52.5%). Death rate at 1 year was 26.8%, while the predicted 1‐year mortality by BCN‐Bio‐HF and MAGGIC‐HF was 17.0% and 22.1%, respectively. BCN‐Bio‐HF AUC was 0.66 (Harrell's C‐statistic 0.64), and MAGGIC‐HF AUC was 0.62 (Harrell's C‐statistic 0.61). Conclusions: The two evaluated risk scores showed suboptimal discrimination and calibration with an underestimation of risk in advanced HF patients receiving levosimendan as destination therapy. There is a need for specific scores for advanced HF. [ABSTRACT FROM AUTHOR]

Purpose. The present scoping review aims to describe and analyze available clinical data on the most commonly reported risk prediction indices in vascular surgery for perioperative mortality, with a particular focus on ruptured abdominal aortic aneurysm (rAAA). Materials and Methods. A scoping review following the PRISMA Protocols Extension for Scoping Reviews was performed. Available full-text studies published in English in PubMed, Cochrane and EMBASE databases (last queried, 30 March 2023) were systematically reviewed and analyzed. The Population, Intervention, Comparison, Outcome (PICO) framework used to construct the search strings was the following: in patients with aortic pathologies, in particular rAAA (population), undergoing open or endovascular surgery (intervention), what different risk prediction models exist (comparison), and how well do they predict post-operative mortality (outcomes)? Results. The literature search and screening of all relevant abstracts revealed a total of 56 studies in the final qualitative synthesis. The main findings of the scoping review, grouped by the risk score that was investigated in the original studies, were synthetized without performing any formal meta-analysis. A total of nine risk scores for major vascular surgery or elective AAA, and 10 scores focusing on rAAA, were identified. Whilst there were several validation studies suggesting that most risk scores performed adequately in the setting of rAAA, none reached 100% accuracy. The Glasgow aneurysm score, ERAS and Vancouver score risk scores were more frequently included in validation studies and were more often used in secondary studies. Unfortunately, the published literature presents a heterogenicity of results in the validation studies comparing the different risk scores. To date, no risk score has been endorsed by any of the vascular surgery societies. Conclusions. The use of risk scores in any complex surgery can have multiple advantages, especially when dealing with emergent cases, since they can inform perioperative decision making, patient and family discussions, and post hoc case-mix adjustments. Although a variety of different rAAA risk prediction tools have been published to date, none are superior to others based on this review. The heterogeneity of the variables used in the different scores impairs comparative analysis which represents a major limitation to understanding which risk score may be the "best" in contemporary practice. Future developments in artificial intelligence may further assist surgical decision making in predicting post-operative adverse events. [ABSTRACT FROM AUTHOR]

Background There is a need to develop electronic health record–based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Results We studied adults with heart failure (HF) from 2011 to 2019 within an integrated health care delivery system. WHF encounters were ascertained using natural language processing and structured data. We conducted boosted decision tree ensemble models to predict 1‐year hospitalizations, emergency department visits/observation stays, and outpatient encounters for WHF and all‐cause death within each LVEF category: HF with reduced ejection fraction (EF) (LVEF

Background: Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. Objective: To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. Design: Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. Subjects: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. Main Measures: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). Key Results: A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. Conclusions: Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients. [ABSTRACT FROM AUTHOR]

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The Cyber-Physical System (CPS) is poised to have a revolutionary impact on almost every area of our lives including transportation, healthcare, energy, automation and other industrial sectors. However, securing the CPS has emerged as a major area of concern. A thorough review on the various security scenarios in CPS, the attacks, the diverse approaches to model different attacks, and the need for CPS testbeds has been presented in this paper. The significance of risk models and risk assessment has also been detailed. The intricate research challenges that are faced in securing the CPS have also been presented. [ABSTRACT FROM AUTHOR]

The US Environmental Protection Agency (USEPA or the Agency) is responsible for administering the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA). The Agency is also required to assess the potential risks of pesticides undergoing registration or re‐registration to threatened and endangered (i.e., listed) species to ensure compliance with the Endangered Species Act. To assess potential risks to listed species, a screening‐level risk assessment in the form of a biological evaluation (BE) is undertaken by the Agency for each pesticide. Given the large number of registration actions handled by the USEPA annually, efficient tools for conducting BEs are desirable. However, the "Revised Method" that is the basis for the USEPA's BE process has been ineffective at filtering out listed species and critical habitats that are at de minimis risk to pesticides. In the USEPA's BEs, the Magnitude of Effect Tool (MAGtool) has been used to determine potential risks to listed species that potentially co‐occur with pesticide footprints. The MAGtool is a highly prescriptive, high‐throughput compilation of existing FIFRA screening‐level models with a geospatial interface. The tool has been a significant contributor to risk inflation and ultimately process inefficiency. The ineffectiveness of the tool stems from compounding conservatism, unrealistic and unreasonable assumptions regarding usage, limited application of species‐specific data, lack of consideration of multiple lines of evidence, and inability to integrate higher‐tier data. Here, we briefly describe the MAGtool and the critical deficiencies that impair its effectiveness, thus undermining its intention. Case studies are presented to highlight the deficiencies and solutions are recommended for improving listed species assessments in the future. Integr Environ Assess Manag 2023;19:817–829. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Point: When evaluating pesticide risk to threatened and endangered species and their critical habitat, the USEPA requires adjustments to their biological evaluation approach to achieve an efficient and scientifically defensible process. [ABSTRACT FROM AUTHOR]

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models. [ABSTRACT FROM AUTHOR]

In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship's systems affect the outcome of each model. [ABSTRACT FROM AUTHOR]

Develop a stakeholder-informed ethical framework to provide practical guidance to health systems considering implementation of suicide risk prediction models. In this multi-method study, patients and family members participating in formative focus groups (n = 4 focus groups, 23 participants), patient advisors, and a bioethics consultant collectively informed the development of a web-based survey; survey results (n = 1,357 respondents) and themes from interviews with stakeholders (patients, health system administrators, clinicians, suicide risk model developers, and a bioethicist) were used to draft the ethical framework. Clinical, ethical, operational, and technical issues reiterated by multiple stakeholder groups and corresponding questions for risk prediction model adopters to consider prior to and during suicide risk model implementation are organized within six ethical principles in the resulting stakeholder-informed framework. Key themes include: patients' rights to informed consent and choice to conceal or reveal risk (autonomy); appropriate application of risk models, data and model limitations and consequences including ambiguous risk predictors in opaque models (explainability); selecting actionable risk thresholds (beneficence, distributive justice); access to risk information and stigma (privacy); unanticipated harms (non-maleficence); and planning for expertise and resources to continuously audit models, monitor harms, and redress grievances (stewardship). Enthusiasm for risk prediction in the context of suicide is understandable given the escalating suicide rate in the U.S. Attention to ethical and practical concerns in advance of automated suicide risk prediction model implementation may help avoid unnecessary harms that could thwart the promise of this innovation in suicide prevention. Patients' desire to consent/opt out of suicide risk prediction models. Recursive ethical questioning should occur throughout risk model implementation. Risk modeling resources are needed to continuously audit models and monitor harms. [ABSTRACT FROM AUTHOR]

Recurrent events can occur more than once in the same individual; such events may be of different types, known as multitype recurrent events. They are very common in longitudinal studies. Often there is a terminating event, after which no further events can occur. The risk of any event, including terminating events such as death or cure, is typically affected by prior events. We propose a flexible joint multitype recurrent-events model that explicitly provides estimates of the change in risk for each event due to subject characteristics, including number and type of prior events and the absolute risk for every event type (terminating and nonterminating), and predicts event-free survival probability over a desired time period. The model is fully parametric, and therefore a standard likelihood function and robust standard errors can be constructed. We illustrate the model with applications to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (1994–2002) and provide discussion of the results and model features. [ABSTRACT FROM AUTHOR]

Human–wildlife interactions can have negative consequences when they involve large carnivores. Spatial risk modelling could serve as a useful management approach for predicting and pre‐emptively mitigating negative interactions. We present a mechanistic modelling framework and examine interactions between humans and sloth bears (Melursus ursinus) in a multi‐use forest landscape of central India. We first assessed patterns and determinants of bear distribution across the landscape using indirect sign surveys. At the same spatial scale, we then estimated spatial probabilities of bear attacks on people using information from 675 interviews with local residents, incorporating estimates of distribution probabilities from the previous step. We found the average occupancy probability across 128 grid‐cells to be 0.77 (SE = 0.03). Bear occupancy was influenced by terrain ruggedness, forest composition and configuration, vegetation productivity and size of human settlements. The average probability of a bear attack in any given grid‐cell was 0.61 (SE = 0.03), mostly determined by bear occurrence patterns, forest cover, terrain ruggedness, and size of human settlements. Using spatial information on people's dependence on forest resources, we identified locations with the highest risk of bear attacks. Our study demonstrates that human attacks by bears—generally believed to be random or incidental—in fact showed deterministic patterns. Our framework can be applied to other scenarios involving human–wildlife conflicts. Based on our findings, we propose that a proactive co‐management approach which involves collaboration between wildlife managers and local residents could help better manage human–bear conflicts in central India and elsewhere across the species' range. [ABSTRACT FROM AUTHOR]

This study looks into the many methods that are used in the risk assessment procedure that is used in the construction industry nowadays. As a result of the slow adoption of novel assessment methods, professionals frequently resort to strategies that have previously been validated as being successful. When it comes to risk assessment, having a precise analytical tool that uses the cost of risk as a measurement and draws on the knowledge of professionals could potentially assist bridge the gap between theory and practice. This step will examine relevant literature, sort articles according to their published year, and identify domains and qualities. Consequently, the most significant findings have been presented in a manner that is consistent with logic and is predicated on the temporal evolution between 1990 and 2015.

BackgroundThe increase in lung cancer screening is intensifying the need for a noninvasive test to characterize the many indeterminate pulmonary nodules (IPN) discovered. Correctly identifying non-cancerous nodules is needed to reduce overdiagnosis and overtreatment. Alternatively, early identification of malignant nodules may represent a potentially curable form of lung cancer.ObjectiveTo develop and validate a plasma-based multiplexed protein assay for classifying IPN by discriminating between those with a lung cancer diagnosis established pathologically and those found to be clinically and radiographically stable for at least one year.MethodsUsing a novel technology, we developed assays for plasma proteins associated with lung cancer into a panel for characterizing the risk that an IPN found on chest imaging is malignant. The assay panel was evaluated with a cohort of 277 samples, all from current smokers with an IPN 4-30 mm. Subjects were divided into training and test sets to identify a Support Vector Machine (SVM) model for risk classification containing those proteins and clinical factors that added discriminatory information to the Veteran's Affairs (VA) Clinical Factors Model. The algorithm was then evaluated in an independent validation cohort.ResultsAmong the 97 validation study subjects, 68 were grouped as having intermediate risk by the VA model of which the SVM model correctly identified 44 (65%) of these intermediate-risk samples as low (n=16) or high risk (n=28). The SVM model negative predictive value (NPV) was 94% and its sensitivity was 94%.ConclusionThe performance of the novel plasma protein biomarker assay supports its use as a noninvasive risk assessment aid for characterizing IPN. The high NPV of the SVM model suggests its application as a rule-out test to increase the confidence of providers to avoid aggressive interventions for their patients for whom the VA model result is an inconclusive, intermediate risk.

BackgroundIn the National Lung Screening Trial, 96.4% of nodules had benign etiology. To avoid unnecessary actions and exposure to harm, individuals with benign disease must be identified. We describe herein the analytical validation of a multi-analyte immunoassay for characterizing the risk that a lung nodule found on CT is malignant. Those at lower risk may be considered for serial surveillance to avoid unnecessary and potentially harmful procedures. While those nodules characterized at higher risk may be appropriate for more aggressive actions.ObjectiveTo validate the analytical performance of multiplexed plasma protein assays used in a novel test for lung nodule characterization.MethodsA multiplexed immunoassay panel for the measurement of plasma proteins in current smokers who present with a lung nodule on CT scan was evaluated in a clinical testing laboratory. Assay analytical sensitivity, reproducibility, precision, and recovery of Epidermal Growth Factor Receptor (EGFR), Prosurfactant protein B (ProSB), and Tissue Inhibitor of Metalloproteinases 1 (TIMP1) from human EDTA plasma samples were evaluated across multiple runs, lots, and technicians. Interfering substances and sample pre-analytical storage conditions were evaluated for their effect on analyte recovery. The lung nodule risk score reproducibility was assessed across multiple lots.ResultsThe assay sensitivities were 0.10 ng/mL EGFR, 0.02 ng/mL ProSB, and 0.29 ng/mL TIMP1 with over three orders of magnitude in the assay dynamic ranges. The assays and analytes are robust to pre-analytical sample handling and the plasma can be stored for up to 4 days at 4°C either when freshy collected or thawed after long-term storage at -80°C. Total imprecision after 20 days of testing remained under 9% for all three assays. Risk score variability remained within a ± 10% risk score range.ConclusionsThe three protein assays comprising the multi-analyte plasma test for lung nodule characterization performed quite acceptably in a clinical laboratory.

Aotearoa New Zealand's response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub, was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country's pandemic response. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation. This framework necessarily canvassed a broad range of perspectives, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives. To our knowledge, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. This paper describes the experiences and lessons learned through this process from the perspective of governance group members, emphasising the role of robust governance processes in building a high-trust platform that enables rapid translation of algorithms from research to practice. [ABSTRACT FROM AUTHOR]

Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation. [ABSTRACT FROM AUTHOR]

The increase in the frequency, duration and severity of disturbances can disrupt forest composition and structure, posing threats to ecosystem services. Adaptive forest management has the potential to adjust forests to future disturbance regimes, by reducing their susceptibility to these threats and, therefore, increasing their adaptation capacity. However, few comprehensive studies have integrated the factors involved in adaptive forest management by comparing the biomass of national parks of the same region, or examined how global change might affect them in the near future. In this study, we integrate data of forest inventories, wildfires, high-resolution climate and pests information in climate model projections to assess climate-sensitive risks associated with key abiotic disturbances (such as fires, droughts and pests) in coniferous Mediterranean mountain forests, considering various forest management approaches and climate scenarios in three Spanish national parks. We employed the conceptual framework introduced by Lecina-Diaz et al. (2021a) to evaluate risks to forests and considered Exposed values (E), Hazard Magnitude (HM), Susceptibility (S) and Lack of Adaptive Capacity (LAC). We applied statistical weights using expert weights and the analytic hierarchy process (AHP) as the basis for determining the global ranking score of each indicator of the risk components. We extracted information about the climatic scenarios from the Coupled Model Intercomparison Project Phase 6. Three forest management scenarios were generated, named "low intervention», «traditional silviculture", and "reduction of climate vulnerability", and mapped the risks of carbon sequestration for the different scenarios. The importance of each component was estimated using the AHP. HM was most significantly affected by the Aridity Index and the Fire Risk Index, with weights of 27.7 % and 24.2 %, respectively; S was affected by fuel load (29.0 %) and the Suppression Difficulty index (21.0 %), and LAC was influenced by forest management (32.0 %) and resprouting capacity (22.0 %). The three forest management scenarios exhibited different risk values that decreased with increasing intensity of the intervention in the three protected areas. Likewise, when two climate scenarios were considered, risk increased in the protected areas (Teide National Park, 10.69 %; Sierra Nevada National Park, 10.9 %; Sierra de las Nieves National Park, 6.17 %). Patterns of spatial arrangement of risk, which included HM, S and LAC, were consistent with the risk level, with the highest risk values being concentrated in the buffer areas (natural parks), with areas with a greater presence of coniferous plantations. The risk maps allowed us to identify areas that are critical under extreme conditions and where the greatest disturbance events may occur. Our study underscores that risks of disturbances in mountain ecosystems dominated by conifers within protected areas can be mitigated by forest management. The application of forest management adaptation strategies in national parks and buffer areas has proven effective in reducing risks of disturbances and preserving certain ecosystem services, such as carbon sequestration. • Reducing risk susceptibility enhances the adaptation of forests. • Forest management modulates the impacts of global change on these ecosystems. • Climate-sensitive risks were associated with abiotic disturbances on mountain forests. • A risk assessment framework was used to determine the risk ranking. • Forest management contributes to mitigate risks and preserving ecosystem services. [ABSTRACT FROM AUTHOR]

Aims: Various cardiovascular risk prediction models have been developed for patients with type 2 diabetes mellitus. Yet few models have been validated externally. We perform a comprehensive validation of existing risk models on a heterogeneous population of patients with type 2 diabetes using secondary analysis of electronic health record data. Methods: Electronic health records of 47,988 patients with type 2 diabetes between 2013 and 2017 were used to validate 16 cardiovascular risk models, including 5 that had not been compared previously, to estimate the 1-year risk of various cardiovascular outcomes. Discrimination and calibration were assessed by the c-statistic and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. Each model was also evaluated based on the missing measurement rate. Sub-analysis was performed to determine the impact of race on discrimination performance. Results: There was limited discrimination (c-statistics ranged from 0.51 to 0.67) across the cardiovascular risk models. Discrimination generally improved when the model was tailored towards the individual outcome. After recalibration of the models, the Hosmer-Lemeshow statistic yielded p-values above 0.05. However, several of the models with the best discrimination relied on measurements that were often imputed (up to 39% missing). Conclusion: No single prediction model achieved the best performance on a full range of cardiovascular endpoints. Moreover, several of the highest-scoring models relied on variables with high missingness frequencies such as HbA1c and cholesterol that necessitated data imputation and may not be as useful in practice. An open-source version of our developed Python package, cvdm, is available for comparisons using other data sources.

Introduction of an animal viral disease, especially a notifiable disease, into an importing country or region free from the disease may lead to serious epidemiological consequences and economic losses. Trade in live animals is historically considered one of the most important risk pathways. To estimate the magnitude of such risk, the likelihood of a virus’ entry into a country and the consequences of this event should be jointly evaluated. Depending on data availability, the urgency of the problem and the detail level of the objectives, a risk assessment may be conducted in a qualitative, semi-quantitative or quantitative way. The purpose of this review was firstly to provide a brief description of each step of the risk analysis process, with particular emphasis on the risk assessment component, and subsequently to supply examples of different approaches to the assessment of the risk of the introduction of selected animal viral diseases. Based on the reviewed models, the overall likelihood of introduction of particular diseases was generally estimated as low. The output risk value was strongly dependent on the duration of the silent phase of the epidemic in the country of origin. Other parameters with some bearing upon the risk derived from the epidemiological situation in the country of origin and the biosecurity or mitigation measures implemented in the country of destination. The investigated models are universal tools for conducting assessment of the risk of introduction of various animal diseases to any country. Their application may lead to timely implementation of appropriate measures for the prevention of the spread of a disease to another country or region.

Objective: To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. Methods: Risk models were identified from a recent systematic review and the Cancer‐PRS web directory. A narrative synthesis of the models, previous validation studies and related genome‐wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). Results: A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic‐only models (seven of 25) and most of the mixed genetic‐phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. Conclusions: Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined. [ABSTRACT FROM AUTHOR]

Simple Summary: We undertook an individual patient data meta-analysis of the overall survival of 1216 patients with PSA-only recurrence of prostate cancer restaged with PSMA PET/CT before salvage treatment. Despite the patients having a low PSA at the recurrence, the restaging PSMA PET/CT markedly predicted the overall survival for the patients with a prescan PSA > 0.5 ng/mL. An individual patient meta-analysis followed 1216 patients with PSA-only recurrence (biochemical recurrence, BCR) restaged with [68Ga]Ga-PSMA-11 PET/CT before the salvage treatment for median 3.5 years and analyzed the overall survival (OS). A new risk model included a good risk group with a prescan PSA < 0.5 ng/mL (26%), an intermediate risk group with a prescan PSA > 0.5 ng/mL and a PSMA PET/CT with 1 to 5 positive sites (65%), and a poor risk group with a prescan PSA > 0.5 ng/mL and a PSA PET/CT with > 5 positive sites (9%) (p < 0.0001, log rank test). The poor risk group had a five-year OS > 60%. Adding a BCR risk score by the European Association of Urology did not significantly improve the prediction of OS (p = 0.64). In conclusion, the restaging PSMA PET/CT markedly predicted the 5-year OS. The new risk model for patients with PSA-only relapse requires a restaging PSMA PET/CT for patients with a prescan PSA > 0.5 ng/mL and has a potential use in new trials aiming to improve the outcome for patients with PSA-only recurrence who have polysites prostate cancer detected on PSMA PET/CT. [ABSTRACT FROM AUTHOR]

Objective：To investigate the clinicopathological features and prognosis-related factors of the patients with solitary fibrous tumor(SFT), and to provide the evidence for its pathological diagnosis, clinical treatment and prognosis judgment. Methods：The clinicopathological data of 86 patients with SFT who had undergone surgical resection from different systems were collected and divided into low(n=65), medium (n=14) and high risk groups(n=7) according to the risk classification criteria. The general morphology of tumor was observed, HE staining and immunohistochemical staining were used to detect the morphology of SFT, and the follow-up data of the patients were obtained for the prognosis-related correlation analysis. Kaplan-Meier survival curve method was used to analyze the relationship between single clinicopathological factor in different parts and progression free survival of the patients, and Cox regression analysis was used to analyze the relationships between multiple factors and progression free survival of the patients. Results：Of the 86 cases of SFT, 37 were male and 49 were female. There was no significant difference in the gender constituent ratio of the patients with SFT at different sites (P> 0. 05). There was no significant difference in the age of patients with SFT at different sites(P>0. 05). There was no significant difference in the distribution of SFT at different sites among low, medium, and high risk groups(P>0. 05). There was significant statistical difference in the tumor diameter at different sites(P<0. 01). The microscope results showed that the shape and arrangement of tumor cells were diverse, and the spindle or oval cells were not arranged structurally in varying density；the characteristic antler like branching vessels and collagen fibers of varying thickness were common；most of the tumor cells were mild in shape and heterotypic, and the mitotic image was not obvious. The immunohistochemiscal staining results showed that the STAT-6 nucleus was diffusely and strongly positive；CD34, Bcl-2 and CD99 were positive in different degrees. A total of 61 cases were followed up for 2－139 months. Among them, 10 cases recurred, and the recurrence rates were 9% in low risk group, 14% in medium risk group, and 28% in high risk group, respectively. The univariate analysis results showed that there was a significant difference in the progression free survival between the patients with mitotic images<4/10 HPF and those with mitotic images ≥ 4/10 HPF (P<0. 05)；there were significant differences in the progression free survival between high risk group and low, medium risk groups(P<0. 05). The multivariate analysis results showed that gender, age, tumor diameter and mitotic count were not the independent predictors of progression free survival of the patients(P>0. 05). Conclusion：SFT can occur in many organs and systems of human body, and its morphology is diverse. The diameters of tumors in the central nervous system, upper respiratory tract and orbit are significantly smaller than those in female genital tract, abdominal cavity, subcutaneous soft tissue, lung and pleura. STAT-6 is a specific and sensitive index for SFT diagnosis；mitotic images ≥ 4/10 HPF and high risk classification are the risk factors for the progression free survival shortening；multivariate comprehensive analysis of tumor risk classification can not fully reflect the prognosis of the patients. [ABSTRACT FROM AUTHOR]

Individuals eligible for lung cancer screening (LCS) are at risk for atherosclerotic cardiovascular disease (ASCVD) due to smoking history. Coronary artery calcifications (CAC), a common incidental finding on low-dose CT (LDCT) for LCS, is a predictor of cardiovascular events. Despite findings of high ASCVD risk and CAC, a substantial proportion of LCS patients are not prescribed primary preventive statin therapy for ASCVD. We assessed the frequency of statin prescription in LCS patients with moderate levels of CAC. Among 259 individuals with moderate CAC, 95% had ASCVD risk ≥ 7.5%. Despite this, 27% of patients were statin-free prior to LDCT and 21.2% remained statin-free after LDCT showing moderate CAC. Illustratively, while a substantial proportion of LCS patients are statin-eligible, many lack a statin prescription, even after findings of CAC burden. CAC reporting should be standardized, and interdisciplinary communication should be optimized to ensure that LCS patients are placed on appropriate preventive therapy. [ABSTRACT FROM AUTHOR]

Aims: This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clinical risk calculation tool was subsequently developed based on these findings., Methods and Results: This nested case-control study included 200 patients with chronic heart failure (CHF) from the China-Japan Friendship Hospital (September 2019 to December 2022). Sixty-five variables were collected, including basic information, physical and chemical examinations, and quality of life assessments. WHF occurrence within a 3-month follow-up was the outcome event. Variables were screened using LASSO regression, univariate analysis, and comparison of key variables in multiple ML models. Eighty per cent of the data was used for training and 20% for testing. The best models were identified by integrating nine ML algorithms and interpreted using SHAP, and to develop a final risk calculation tool. Among participants, 68 (34.0%) were female, with a mean age (standard deviation, SD) of 68.57 (12.80) years. During the follow-up, 60 participants (30%) developed WHF. N-terminal pro-brain natriuretic peptide (NT-proBNP), creatinine (Cr), uric acid (UA), haemoglobin (Hb), and emotional area score on the Minnesota Heart Failure Quality of Life Questionnaire were critical predictors of WHF occurrence. The random forest (RF) model was the best model to predict WHF with an area under the curve (AUC) (95% confidence interval, CI) of 0.842 (0.675-1.000), accuracy of 0.775, sensitivity of 0.900, specificity of 0.833, negative predictive value of 0.800, and positive predictive value of 0.600 for the test set. SHAP analysis highlighted NT-proBNP, UA, and Cr as significant predictors. An online risk predictor based on the RF model was developed for personalized WHF risk assessment., Conclusions: This study identifies NT-proBNP, Cr, UA, Hb, and emotional area scores as crucial predictors of WHF in CHF patients. Among the nine ML algorithms assessed, the RF model showed the highest predictive accuracy. SHAP analysis further emphasized NT-proBNP, UA, and Cr as the most significant predictors. An online risk prediction tool based on the RF model was subsequently developed to enhance early and personalized WHF risk assessment in clinical settings., (© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: Surgery for established cataract is highly cost-effective and uncontroversial, yet uncertainty remains for individuals about when to proceed and when to delay surgery during the earlier stages of cataract. Objective: We aimed to improve decision-making for cataract surgery through the development of evidence-based clinical tools that provide general information and personalised risk/benefit information. Design: We used a mixed methodology consisting of four work packages. Work package 1 involved the development and psychometric validation of a brief, patient self-reported measure of visual difficulty from cataract and its relief from surgery, named Cataract Patient-Reported Outcome Measure, five items (Cat-PROM5). Work package 2 involved the review and refinement of risk models for adverse surgical events (posterior capsule rupture and visual acuity loss related to cataract surgery). Work package 3 involved the development of prediction models for the Cat-PROM5-based self-reported outcomes from a cohort study of 1500 patients; assessment of the validity of preference-based health economic indices for cataract surgery and the calibration of these to Cat-PROM5; assessment of patients’ and health-care professionals’ views on risk–benefit presentation formats, the perceived usefulness of Cat-PROM5, the value of personalised risk–benefit information, high-value information items and shared decision-making; development of cataract decision aid frequently asked questions, incorporation of personalised estimates of risks and benefits; and development of a cataract decision quality measure to assess the quality of decision-making. Work package 4 involved a mixed-methods feasibility study for a fully powered randomised controlled trial of the use of the cataract decision aid and a qualitative study of discordant or mismatching perceptions of outcome between patients and health-care professionals. Setting: Four English NHS recruitment centres were involved: Bristol (lead centre), Brighton, Gloucestershire and Torbay. Multicentre NHS cataract surgery data were obtained from the National Ophthalmology Database. Participants: Work package 1 – participants (n = 822) were from all four centres. Work package 2 – electronic medical record data were taken from the National Ophthalmology Database (final set > 1M operations). Work package 3 – cohort study participants were from Bristol (n = 1200) and Gloucestershire (n = 300); qualitative and development work was undertaken with patients and health-care professionals from all four centres. Work package 4 – Bristol, Brighton and Torbay participated in the recruitment of patients (n = 42) for the feasibility trial and recruitment of health-care professionals for the qualitative elements. Interventions: For the feasibility trial, the intervention was the use of the cataract decision aid, incorporating frequently asked questions and personalised estimations of both adverse outcomes and self-reported benefit. Main outcome measures: There was a range of quantitative and qualitative outcome measures: questionnaire psychometric performance metrics, risk indicators of adverse surgical events and visual outcome, predictors of self-reported outcome following cataract surgery, patient and health-care practitioner views, health economic calibration measures and randomised controlled trial feasibility measures. Data sources: The data sources were patient self-reported questionnaire responses, study clinical data collection forms, recorded interviews with patients and health-care professionals, and anonymised National Ophthalmology Database data. Results: Work package 1 – Cat-PROM5 was developed and validated with excellent to good psychometric properties (Rasch reliability 0.9, intraclass correlation repeatability 0.9, unidimensionality with residual eigenvalues ≤ 1.5) and excellent responsiveness to surgical intervention (Cohen delta –1.45). Work package 2 – earlier risk models for posterior capsule rupture and visual acuity loss were broadly affirmed (C-statistic for posterior capsule rupture 0.64; visual acuity loss 0.71). Work package 3 – the Cat-PROM5-based self-reported outcome regression models were derived based on 1181 participants with complete data (R2 ≈ 30% for each). Of the four preference-based health economic indices assessed, two demonstrated reasonable performance. Cat-PROM5 was successfully calibrated to health economic indices; adjusted limited dependent variable mixture models offered good to excellent fit (root-mean-square error 0.10–0.16). The personalised quantitative risk information was generally perceived as beneficial. A cataract decision aid and cataract decision quality measure were successfully developed based on the views of patients and health-care professionals. Work package 4 – data completeness was good for the feasibility study primary and secondary variables both before and after intervention/surgery (data completeness range 100–88%). Considering ability to recruit, the sample size required, instrumentation and availability of necessary health economic data, a fully powered randomised controlled trial (patients, n = 800, effect size 0.2 standard deviations, power 80%; p = 0.05) of the cataract decision aid would be feasible following psychometric refinement of the primary outcome (the cataract decision quality measure). The cataract decision aid was generally well-received by patients and health-care professionals, with cautions raised regarding perceived time and workload barriers. Discordant outcomes mostly related to patient dissatisfaction, with no clinical problem found. Limitations: The National Ophthalmology Database data are expected to include some errors (mitigated by large multicentre data aggregations). The feasibility randomised controlled trial primary outcome (the cataract decision quality measure) displayed psychometric imperfections requiring refinement. The clinical occurrence of discordant outcomes is uncommon and the study team experienced difficulty identifying patients in this situation. Future work: Future work could include regular review of the risk models for adverse outcomes to ensure currency, and the technical precision of complex-numbers analysis of refractive outcome to invite opportunities to improve post-operative spectacle-free vision. In addition, a fully powered randomised controlled trial of the cataract decision aid would be feasible, following psychometric refinement of the primary outcome (the cataract decision quality measure); this would clarify its potential role in routine service delivery. Conclusions: In this research programme, evidence-based clinical tools have been successfully developed to improve pre-operative decision-making in cataract surgery. These include a psychometrically robust, patient-reported outcome measure (Cat-PROM5); prediction models for patient self-reported outcomes using Cat-PROM5; prediction models for clinically adverse surgical events and adverse visual acuity outcomes; and a cataract decision aid with relevant general information and personalised risk/benefit predictions. In addition, the successful mapping of Cat-PROM5 to existing health economic indices was achieved and the performances of indices were assessed in patients undergoing cataract surgery. A future full-powered randomised controlled trial of the cataract decision aid would be feasible (patients, n = 800, effect size 0.2 standard deviations, power 80%; p = 0.05). Trial registration: This trial is registered as ISRCTN11309852. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 10, No. 9. See the NIHR Journals Library website for further project information.

Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service. [ABSTRACT FROM AUTHOR]

The BE-ALIVE score is an additive scoring system for estimating 30-day mortality in patients presenting with an acute coronary syndrome (ACS) [ 1 ]. However, it had only previously been tested on an internal validation cohort. The aim was to assess the scoring system on an external validation cohort. The scoring system comprises six domains: (1) Base Excess (1 point for < −2 mmols/L), (2) Age (<65 years: 0 points, 65–74: 1 point, 75–84: 2 points, ≥ 85: 3 points), (3) Lactate (<2 mmols/L: 0 points, 2–4.9: 1 point, 5–9.9: 3 points, ≥ 10: 6 points), (4) Intubated & Ventilated (2 points), (5) Left Ventricular function (normal or mildly impaired: -1 point, moderately impaired: 1 point, severely impaired: 3 points) and (6) External / out of hospital cardiac arrest (1 point). We applied the BE-ALIVE score was applied to 205 consecutive patients at a different institution. Calibration was strong, with an observed to expected ratio of 1.01, a calibration slope of 1.26 and calibration in the large of −0.03. The Spiegelhalter's Z -statistic was −0.95 (p = 0.34). The AUC was 0.95 (0.92–0.98) in the external validation cohort versus 0.90 (0.85–0.95) during internal validation. Overall performance was excellent with a Brier score of 0.07 versus 0.06 during internal validation. The negative predictive value for 30-day mortality of a BE-ALIVE score < 4 was 98 %, with a positive predicted value of a score ≥ 10 of 95 %. The BE-ALIVE score remains a robust predictor of 30-day mortality in an external validation cohort. • What is already known: The BE-ALIVE score was developed to predict 30-day mortality in patients presenting with acute coronary syndromes. • What is unknown: The BE-ALIVE score has not been tested on an external validation cohort. • What this study adds: This study shows that the BE-ALIVE score maintains its discrimination and calibration when tested on an external validation cohort. • Clinical implications: The BE-ALIVE score is a robust and reliable method of predicting 30-day mortality in patients presenting with acute coronary syndromes. [ABSTRACT FROM AUTHOR]

In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship’s systems affect the outcome of each model.

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models.

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools. [ABSTRACT FROM AUTHOR]